Cancer classification with tissue of origin thresholding

Abstract

Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of tissue of origin (TOO) classes. In some embodiments, the plurality of TOO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high tissue signal are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to tissue signal and applies binary threshold cutoffs determined for each stratum.

1 .- 44 . (canceled) 
     
     
         45 . A method for predicting a presence or absence of cancer in a test sample, the method comprising:
 accessing the test sample having a cancer score and a tissue signal for a first tissue label;   selecting one of a plurality of strata based on the tissue signal for the first tissue label, the plurality of strata including a high signal stratum for the first tissue label and a low signal stratum of for the first tissue label; and   predicting whether the test sample is associated with a presence or absence of cancer by comparing the cancer score against a binary threshold cutoff for the selected stratum.   
     
     
         46 . The method of  claim 45 , wherein the test sample comprises a test feature vector determined according to methylation sequencing data of the test sample. 
     
     
         47 . The method of  claim 46 , wherein the cancer score is determined by applying a binary cancer classifier to the test feature vector. 
     
     
         48 . The method of  claim 46 , wherein the tissue signal is a tissue of origin (TOO) prediction determined by applying a multiclass cancer classifier to the test feature vector. 
     
     
         49 . The method of  claim 48 , wherein the TOO prediction comprises a prediction value for each of a plurality of tissue labels, each prediction value indicating a likelihood that the test sample corresponds to a cancer type associated with the tissue label. 
     
     
         50 . The method of  claim 49 , wherein selecting one of a plurality of strata based on the tissue signal for the first tissue label comprises:
 determining whether the tissue signal for the first tissue label is at or above a prediction value threshold;   responsive to determining that the tissue signal for the first tissue label is at or above the prediction value threshold, selecting the high signal stratum; and   responsive to determining that the tissue signal for the first tissue label is below the prediction value threshold, selecting the low signal stratum.   
     
     
         51 . The method of  claim 48 , wherein the TOO prediction indicates one or more top predictions of one or more tissue labels of the plurality of tissue labels, wherein a top prediction of a tissue label indicates that the test sample is predicted to have a cancer type associated with the tissue label of the top prediction. 
     
     
         52 . The method of  claim 51 , wherein selecting one of the plurality of strata comprises:
 determining whether the first tissue label is a top prediction;   responsive to determining that the first tissue label is the top prediction, selecting the high signal stratum; and   responsive to determining that the first tissue label is not the top prediction, selecting the low signal stratum.   
     
     
         53 . The method of  claim 52 , wherein selecting one of a plurality of strata comprises:
 determining whether the first tissue label is a second top prediction;   responsive to determining that the first tissue label is the second top prediction, selecting the high signal stratum; and   responsive to determining that the first tissue label is not the second top prediction, selecting the low signal stratum.   
     
     
         54 . The method of  claim 45 , wherein the plurality of strata includes a medium signal strata for a medium tissue signal. 
     
     
         55 . The method of  claim 45 , wherein the test sample has a tissue signal for a second tissue class, wherein selecting one of a plurality of strata is further based on the tissue signal for the second tissue label. 
     
     
         56 . The method of  claim 45 , wherein the binary threshold cutoff for each stratum is determined by:
 obtaining a holdout set of samples, each sample having a cancer score and a tissue signal for the first tissue label;   stratifying the holdout set into the plurality of strata based on the tissue signals for the first tissue label of the holdout set of samples;   for each stratum of the plurality of strata:
 sweeping through a domain of cancer scores at a plurality of candidate binary threshold cutoffs by calculating a true positive rate and a false positive rate for each candidate binary threshold cutoff based on the cancer scores of the samples in the stratum, and 
 selecting a binary threshold cutoff from the plurality of candidate binary threshold cutoffs for the stratum based on a false positive budget for the stratum and the calculated false positive rates. 
   
     
     
         57 . (canceled) 
     
     
         58 . A method for detecting and classifying cancer, comprising:
 receiving sequencing data for a biological sample comprising cfDNA fragments;   applying a multiclass classifier to features derived from the sequencing data, wherein the multiclass classifier predicts a probability likelihood for each of a plurality of hematological tissue of origin subtype classes; and   determining, based on the probability likelihoods predicted by the multiclass classifier, a hematological tissue of origin associated with the biological sample.   
     
     
         59 .- 73 . (canceled) 
     
     
         74 . A system for predicting a presence or absence of cancer in a test sample, the system comprising:
 a computer processor; and   a non-transitory computer-readable storage medium storing instructions that, when executed by the computer processor, cause the computer processor to perform operations comprising:
 accessing the test sample having a cancer score and a tissue signal for a first tissue label; 
 selecting one of a plurality of strata based on the tissue signal for the first tissue label, the plurality of strata including a high signal stratum for the first tissue label and a low signal stratum of for the first tissue label; and 
 predicting whether the test sample is associated with a presence or absence of cancer by comparing the cancer score against a binary threshold cutoff for the selected stratum. 
   
     
     
         75 . The system of  claim 74 , wherein the test sample comprises a test feature vector determined according to methylation sequencing data of the test sample. 
     
     
         76 . The system of  claim 75 , wherein the cancer score is determined by applying a binary cancer classifier to the test feature vector. 
     
     
         77 . The system of  claim 75 , wherein the tissue signal is a tissue of origin (TOO) prediction determined by applying a multiclass cancer classifier to the test feature vector. 
     
     
         78 . The system of  claim 77 , wherein the TOO prediction comprises a prediction value for each of a plurality of tissue labels, each prediction value indicating a likelihood that the test sample corresponds to a cancer type associated with the tissue label. 
     
     
         79 . The system of  claim 78 , wherein selecting one of a plurality of strata based on the tissue signal for the first tissue label comprises:
 determining whether the tissue signal for the first tissue label is at or above a prediction value threshold;   responsive to determining that the tissue signal for the first tissue label is at or above the prediction value threshold, selecting the high signal stratum; and   responsive to determining that the tissue signal for the first tissue label is below the prediction value threshold, selecting the low signal stratum.   
     
     
         80 . The system of  claim 77 , wherein the TOO prediction indicates one or more top predictions of one or more tissue labels of the plurality of tissue labels, wherein a top prediction of a tissue label indicates that the test sample is predicted to have a cancer type associated with the tissue label of the top prediction. 
     
     
         81 . The system of  claim 80 , wherein selecting one of the plurality of strata comprises:
 determining whether the first tissue label is a top prediction;   responsive to determining that the first tissue label is the top prediction, selecting the high signal stratum; and   responsive to determining that the first tissue label is not the top prediction, selecting the low signal stratum.   
     
     
         82 . The system of  claim 81 , wherein selecting one of a plurality of strata comprises:
 determining whether the first tissue label is a second top prediction;   responsive to determining that the first tissue label is the second top prediction, selecting the high signal stratum; and   responsive to determining that the first tissue label is not the second top prediction, selecting the low signal stratum.   
     
     
         83 . The system of  claim 74 , wherein the plurality of strata includes a medium signal strata for a medium tissue signal. 
     
     
         84 . The system of  claim 74 , wherein the test sample has a tissue signal for a second tissue class, wherein selecting one of a plurality of strata is further based on the tissue signal for the second tissue label. 
     
     
         85 . The system of  claim 74 , wherein the binary threshold cutoff for each stratum is determined by:
 obtaining a holdout set of samples, each sample having a cancer score and a tissue signal for the first tissue label;   stratifying the holdout set into the plurality of strata based on the tissue signals for the first tissue label of the holdout set of samples;   for each stratum of the plurality of strata:
 sweeping through a domain of cancer scores at a plurality of candidate binary threshold cutoffs by calculating a true positive rate and a false positive rate for each candidate binary threshold cutoff based on the cancer scores of the samples in the stratum, and 
 selecting a binary threshold cutoff from the plurality of candidate binary threshold cutoffs for the stratum based on a false positive budget for the stratum and the calculated false positive rates.